aya-ai-assist

v1.43.0 suspicious
6.0
Medium Risk

Personal AI assistant toolkit — sync, schedule, identity

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several concerning behaviors including significant shell execution risks and obfuscation techniques, indicating potential hidden functionality or security issues. While there is no direct evidence of malicious intent, the overall structure and practices raise suspicion.

  • High shell risk due to subprocess usage
  • Significant obfuscation through base64 decoding
Per-check LLM notes
  • Network: The network calls to an API endpoint suggest interaction with a remote service, which is not inherently suspicious but should be reviewed against the package's stated purpose.
  • Shell: Use of subprocess.run and Popen indicates potential execution of external commands, which could be risky depending on how input is handled, especially if it involves user inputs or untrusted data.
  • Obfuscation: The presence of multiple base64 decoding operations with unusual assertions on the decoded data suggests potential obfuscation or encryption practices that may be used to hide code logic or protect sensitive data, raising suspicion.
  • Credentials: No clear patterns indicative of credential harvesting were detected in the provided snippets.
  • Metadata: The package shows low maintenance effort and lacks a clear author identity, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 28 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 28 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (14291 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 605 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • import httpx resp = httpx.post( f"{server}/rest/api/3/search", auth
  • import httpx resp = httpx.get( f"{server}/rest/api/3/issue/{ticket}",
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • """ try: raw = base64.b64decode(payload, validate=True) except Exception as exc:
  • str_public_hex) raw = base64.b64decode(encrypted) # must not raise assert raw[0] == 2 # N
  • _hex) raw = bytearray(base64.b64decode(encrypted)) raw[33] ^= 0xFF # flip a byte in the ci
  • — not raw JSON raw = base64.b64decode(event["content"], validate=True) assert raw[0] == 2
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • t_proc) _kitt_proc = subprocess.Popen( # noqa: S603 — fixed binary path, validated args
  • try: result = subprocess.run( # noqa: S603 cmd, capture_
  • if xclip: result = subprocess.run( # noqa: S603 [xclip, "-selection", "clipboard"
  • elif xsel: result = subprocess.run( # noqa: S603 [xsel, "--clipboard", "--input"],
  • elif clip: result = subprocess.run([clip], input=text.encode(), check=False) # noqa: S603
  • t cron). """ result = subprocess.run( ["crontab", "-l"], # noqa: S607 capture_ou
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aya-ai-assist 
Create a personal productivity assistant app called 'TaskMaster' using the Python package 'aya-ai-assist'. TaskMaster will help users manage their daily tasks, sync across devices, schedule meetings, and maintain digital identities for secure authentication. Here’s how you can build it step-by-step:

1. **Setup Environment**: Ensure you have Python installed along with the 'aya-ai-assist' package.
2. **User Authentication**: Implement user sign-up and login functionalities using 'aya-ai-assist' for secure identity management.
3. **Task Management**: Allow users to create, edit, delete, and mark tasks as completed. Integrate 'aya-ai-assist' for syncing task data across multiple devices.
4. **Calendar Integration**: Enable users to schedule tasks directly into their calendars. Use 'aya-ai-assist' to handle scheduling and sync calendar events.
5. **Notifications**: Set up notifications for upcoming tasks and meetings. Utilize 'aya-ai-assist' to ensure these notifications are consistent across all devices.
6. **Customization**: Let users personalize their experience with themes and custom settings. Leverage 'aya-ai-assist' for seamless customization options.
7. **Data Privacy**: Emphasize on data privacy by using 'aya-ai-assist' for secure storage and transmission of user data.
8. **Testing & Deployment**: Thoroughly test the app for bugs and usability issues before deploying it on platforms like GitHub or PyPI.

By following these steps, you'll create a robust, user-friendly productivity assistant that leverages the powerful capabilities of 'aya-ai-assist' to enhance daily organization and efficiency.

💬 Discussion Feed

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